Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease

Freezing-of-gait (FOG) and impaired walking are common features of Parkinson’s disease (PD). Provision of external stimuli (cueing) can improve gait, however, many cueing methods are simplistic, increase task loading or have limited utility in a real-world setting. Closed-loop (automated) somatosensory cueing systems have the potential to deliver personalised, discrete cues at the appropriate time, without requiring user input. Further development of cue delivery methods and FOG-detection are required to achieve this. In this feasibility study, we aimed to test if FOG-initiated vibration cues applied to the lower-leg via wearable devices can improve gait in PD, and to develop real-time FOG-detection algorithms. 17 participants with Parkinson’s disease and daily FOG were recruited. During 1 h study sessions, participants undertook 4 complex walking circuits, each with a different intervention: continuous rhythmic vibration cueing (CC), responsive cueing (RC; cues initiated by the research team in response to FOG), device worn with no cueing (NC), or no device (ND). Study sessions were grouped into 3 stages/blocks (A-C), separated by a gap of several weeks, enabling improvements to circuit design and the cueing device to be implemented. Video and onboard inertial measurement unit (IMU) data were analyzed for FOG events and gait metrics. RC significantly improved circuit completion times demonstrating improved overall performance across a range of walking activities. Step frequency was significantly enhanced by RC during stages B and C. During stage C, > 10 FOG events were recorded in 45% of participants without cueing (NC), which was significantly reduced by RC. A machine learning framework achieved 83% sensitivity and 80% specificity for FOG detection using IMU data. Together, these data support the feasibility of closed-loop cueing approaches coupling real-time FOG detection with responsive somatosensory lower-leg cueing to improve gait in PD. Supplementary Information The online version contains supplementary material available at 10.1186/s12938-023-01167-y.


Supplementary Methods: GaitAnalyst Video analysis program
GaitAnalyst is custom video gait analysis software coded in Python3.3, to enable individual steps and other events/gait features to be marked and time-stamped against the video recording using keyboard strokes during video playback, which could be run at half and quarter speeds to increase accuracy of feature analysis.A screen shot of the GaitAnalyst interface is shown below.We provide open access to the software here.Video recordings were fragmented into individual files for each circuit for each participant (4 files per 1hours session per participant) and sound removed.Video recordings were viewed in random order and scored independently by 3 observers who had been trained to recognise the relevant gait features and to use the video analysis software.Each observer could recognise the no-device (ND) group from the video, however they were blinded with regard to the no-cue (NC), responsive cue (RC) and continuous cue (CC) interventions.Therefore, during statistical analysis, the active cueing groups (RC and CC) were compared with the device no-cue (NC) control, to avoid the potential for observer bias.
The GaitVideoAnalysis program is programmed in Python3.3 and is now available for open access.
The program interface is present in Fig. 3.

The GaitAnalyst interface
The main features of the program are: 1) Step timing: while the video is playing, observers can label the left or right steps by pressing the LEFT or RIGHT keys on the keyboard.The program records automatically the time (in milliseconds), the key being pressed and generates a report containing such information (screen shot of report below).This enables calculation of step frequency and symmetry.2) FOG: the start of a gait freezing event is labelled/recording by pressing the UP key.The end of a gait freezing event is recorded as the start of normal walking/stepping (i.e., the subsequent left or right step).3) Invalid time labels: if the observer cannot determine the walking characteristics of the participant due an obscured view or other issue, the observer can press the DOWN key to record this.The duration from this DOWN stroke to a valid event label (i.e., step and/or FOG keystroke) is omitted from the analysis.4) Slow-motion play back: the program allows video playback in normal (actual recorded speed) and slow-motion modes (half and quarter speeds).This feature is particularly useful for accurately labelling left and right stepping and studying shuffling events.
A sample GaitAnalyst report.At the right level Should be shorter (short and sharp).
At the right level: fine inside, but if I was outside I would want to go faster.
It would be good to make the device quieter.I would like the option of a manual trigger as well as the automatic function.

Supplementary Figure 3 .
Design of walking circuits during study stage B. a. Segment 1, timed up-and-go test.b.Segment 2, narrow restrictions.c.Segment 3, walking and turning with distraction.d.Segment 4, Move on instruction, multitasking and passing open doorway.
of walking circuits during study stage C. a. Segment 1, timed up-and-go test.b.Segment 2, narrow restrictions.c.Segment 3, walking and tight turns with distraction.d.Segment 4, Move on instruction, multitasking and passing open doorway.